Autonomous driving features on modern motor vehicles, (e.g., the at least partially automated guiding of motor vehicles), require a high-quality environmental perception of the corresponding environment sensors of the motor vehicle. One essential, frequently used environment sensor in motor vehicles is the radar sensor.
The use of radar sensors in motor vehicles has become commonplace in autonomous driving. Radar sensors today are used, first and foremost, as environment sensors for the medium and larger distance ranges for detecting other traffic participants or major objects located at a distance, angle or relative speed. Such radar data can be incorporated in environmental models or made available directly in motor vehicle systems. Conventional applications of radar data with respect to autonomous driving include, for example, longitudinal control systems, such as adaptive cruise control (ACC) or safety systems.
Conventional radar sensor models are most often expansive and therefore rather clunky after the antennas and the electronic components needed on the antenna, which is the radar front end, have been integrated inside a housing. The electronic components therein mainly constitute the radar transceiver that contains a frequency control (typically comprising a phase lock loop—PLL), mixing means, a low noise amplifier (LNA), and the like; but control modules and digital signal processing components are often also implemented in the proximity of the antenna, for example, in order to be able to hand off already processed sensor data, such as, for example, object lists, to a connected bus, for example, a controller area network (CAN) bus.
The implementation of radar components on semiconductor basis proved difficult for a long time, because it required the use of expensive special semi-conductors, particularly gallium arsenide (GaAs). Smaller radar sensors were proposed, whose entire radar front end is implemented on a single chip utilizing silicon-germanium (SiGe) technology, before solutions utilizing complementary metal-oxide-semiconductor (CMOS) technology became known as well. Such solutions are the result of the extension of CMOS technology to high-frequency applications often also referred to as radio frequency (RF) CMOS. A CMOS radar chip of this kind is implemented in highly miniaturized form and does not utilize expensive special semi-conductors, wherefore it offers clear advantages over other semi-conductor technologies especially in manufacturing. An exemplary implementation of a 77 GHz radar transceiver as a CMOS chip has been described in the article by Jri Lee et al., “A Fully Integrated 77-GHz FMCW Radar Transceiver in 65-nm CMOS Technology,” IEEE Journal of Solid State Circuits 45 (2010), pp. 2746-2755.
After proposing the implementation of the chip and the antenna in a joint package, an extremely cheap, small radar sensor has become possible that is noticeably better able to accommodate structural space requirements and that, due to the short signal paths, has a very low signal-to-noise ratio and is suitable for high frequencies and larger, variable frequency bandwidths. Such miniature radio sensors therefore also lend themselves for use in short-range applications, for example, in the range of 30 cm to 10 m.
It has also been proposed previously to provide such a CMOS transceiver chip and/or a package with CMOS transceiver chip and antenna on a common circuit board with a digital signal processing processor (DSP processor) or to integrate the features of the signal processing processor also in the CMOS transceiver chip. A similar integration is possible for control functions.
To provide an ideal foundation for the driver assistance system and the at least partially automated guiding of the motor vehicle, which, accordingly, is the at least partially automated transverse guidance and the at least partially automated longitudinal guidance, conventional techniques provide for merging the sensor data from a plurality of environment sensors, particularly also a plurality of radar sensors, and determining based thereupon an environmental model of the motor vehicle that usually refers to a field around the motor vehicle that is defined by the range of the environment sensors and that describes said field as a function of the type and quality of the sensor data of the environment sensors. Known are object-based environmental models where the environment is described by objects based on their relative positions vis-a-vis the motor vehicle and the object characteristics assigned to them or by environmental models that subdivide the fields they cover in individual cells and assign them a movement state and additional information (grid-based approach). Hybrid approaches situated between the two approaches are also conceivable and have been described previously. Based on such an environmental model, driver assistance systems that are designed for at least partially automated guidance of the motor vehicle can compute concrete longitudinal guidance and/or transverse guidance interventions, and/or they can compute entire trajectories in advance that any further operation by the motor vehicle utilizes to orient itself. It is understood that, even during the implementation of such a trajectory that was computed in advance, the continuously updated environmental model is analyzed to be able to actively react to changes, particularly including features for the avoidance of accidents and/or the mitigation of the consequences of an accident.
Driver assistance systems are frequently designed offering at least a partial configuration capacity, whereby it is possible, for example, for particular comfort features to be activated and deactivated, for warning and information threshold values to be selected by the driver, and/or for informational outputs to be adjustable in terms of how they are displayed, for example, by adjusting the brightness on an optical display and/or the volume on an acoustic display.
Particularly for driver assistance systems where a completely automated guidance of the motor vehicle is to be implemented, in which, accordingly, the driver is to be provided with the best possible support, there exist extremely complex traffic situations where the selection of the trajectory can be less than optimal, due to difficulties in the interpretation of the sensor data and/or of the environmental model and/or the detection properties of the environment sensors are inadequate, wherefore the best possible driver support cannot be implemented in the best possible manner.